Publications related to Euclid: Testing photometric selection of emission-line galaxy targets<sup>?</sup>

Related publications (18)

Speech Data Selection for Efficient ASR Fine-Tuning using Domain Classifier and Pseudo-Label Filtering

In real-world speech data processing, the scarcity of annotated data and the abundance of unlabelled speech data present a significant challenge. To address this, we propose an efficient data selection pipeline for fine-tuning ASR models by generating pseu ...

Institute of Electrical and Electronics Engineers Inc.2025

Stop Wasting your Cache! Bringing Machine Learning into Cache Computing

Michele Caon

The rapid evolution of Machine Learning (ML) workloads, particularly Deep Neural Networks (DNNs) and Transformer-based models, has intensified demands on computing architectures, highlighting the limitations of traditional von Neumann systems due to the me ...

ACM2025

Insights into environmental quenching at z ∼ 1: an enhancement of faint, low-mass passive galaxies in clusters

Yannick Michael Bahé

Understanding the processes that transform star-forming galaxies into quiescent ones is key to unraveling the role of environment in galaxy evolution. We present measurements of the luminosity functions (LFs) and stellar mass functions (SMFs) of passive re ...

Oxford University Press (OUP)2025

Global Group Fairness in Federated Learning via Function Tracking

Daniel Kuhn, Yves Rychener, Yifan Hu

We investigate group fairness regularizers in federated learning, aiming to train a globally fair model in a distributed setting. Ensuring global fairness in distributed training presents unique challenges, as fairness regularizers typically involve probab ...

2025

Behaviorally Relevant Information Content in Interneuron Activity

Mahsa Barzegarkeshteli

The nematode Caenorhabditis elegans offers a uniquely tractable model for studying the relationships between neuronal activity, connectivity, and behavior. Despite its simplicity, key questions remain about how information flows through its nervous system ...

EPFL2025

Gaussian universality of perceptrons with random labels

Lenka Zdeborová, Florent Gérard Krzakala, Bruno Loureiro, Ludovic Théo Stephan, Federica Gerace

While classical in many theoretical settings-and in particular in statistical physics-inspired works-the assumption of Gaussian i.i.d. input data is often perceived as a strong limitation in the context of statistics and machine learning. In this study, we ...

Amer Physical Soc2024

The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents

Lenka Zdeborová, Florent Gérard Krzakala, Yatin Dandi, Emanuele Troiani, Luca Pesce, Luca Arnaboldi

We investigate the training dynamics of two-layer neural networks when learning multi-index target functions. We focus on multi-pass gradient descent (GD) that reuses the batches multiple times and show that it significantly changes the conclusion about wh ...

JMLR2024

Brief Announcement: A Case for Byzantine Machine Learning

Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot, Sadegh Farhadkhani

The success of machine learning (ML) has been intimately linked with the availability of large amounts of data, typically collected from heterogeneous sources and processed on vast networks of computing devices (also called workers). Beyond accuracy, the u ...

ACM2024

Swift : a modern highly parallel gravity and smoothed particle hydrodynamics solver for astrophysical and cosmological applications

Yves Revaz, Yannick Michael Bahé, Loïc Hausammann, Mladen Ivkovic

Numerical simulations have become one of the key tools used by theorists in all the fields of astrophysics and cosmology. The development of modern tools that target the largest existing computing systems and exploit state-of-the-art numerical methods and ...

2024

Nissl_5, Raw images for Machine learning for histological annotation and quantification of cortical layers

Julie Meystre

This dataset contains images (TIFF image data) of brain juvenile rats Wistar Han (P14) scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F5 ...

Zenodo2024

The connection between mergers and AGN activity in simulated and observed massive galaxies

Michaela Hirschmann

We analyse a suite of 29 high-resolution zoom-in cosmological hydrodynamic simulations of massive galaxies with stellar masses M-star > 10(10.9) M-circle dot, with the goal of better understanding merger activity among active galactic nuclei (AGN), AGN act ...

Oxford Univ Press2023

Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis

Olga Fink, Florent Evariste Forest

Intelligent Fault Diagnosis (IFD) based on deep learning has proven to be an effective and flexible solution, attracting extensive research. Deep neural networks can learn rich representations from vast amounts of representative labeled data for various ap ...

2023

Deep learning-based galaxy image deconvolution

Frédéric Courbin, Pascale Jablonka, Utsav Akhaury

With the onset of large-scale astronomical surveys capturing millions of images, there is an increasing need to develop fast and accurate deconvolution algorithms that generalize well to different images. A powerful and accessible deconvolution method woul ...

FRONTIERS MEDIA SA2022

Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning

Roland Logé, Hossein Ghasemi Tabasi, Rita Drissi Daoudi

This study investigates the use of a low cost microphone combined with state-of-the-art machine learning (ML) algorithms as online process monitoring to differentiate various materials and process regimes of Laser-Powder Bed Fusion (LPBF). Three processing ...

TAYLOR & FRANCIS LTD2022

Offshore wind farm wake modelling using deep feed forward neural networks for active yaw control and layout optimisation

Sokratis Anagnostopoulos

Offshore wind farm modelling has been an area of rapidly increasing interest over the last two decades, with numerous analytical as well as computational-based approaches developed, in an attempt to produce designs that improve wind farm efficiency in powe ...

IOP PUBLISHING LTD2022

Non-locality of the turbulent electromotive force

Abhijit Bhausaheb Bendre

The generation of large-scale magnetic fields ((B) over bar) in astrophysical systems is driven by the mean turbulent electromotive force ((E) over bar), the cross-correlation between local fluctuations of velocity and magnetic fields. This can depend non- ...

2022

Discovery of Slow Variables in a Class Of Multiscale Stochastic Systems Via Neural Networks

Przemyslaw Zielinski, Jan Sickmann Hesthaven

Finding a reduction of complex, high-dimensional dynamics to its essential, low-dimensional "heart" remains a challenging yet necessary prerequisite for designing efficient numerical approaches. Machine learning methods have the potential to provide a gene ...

2022

Non-Structured DNN Weight Pruning--Is It Beneficial in Any Platform?

Shaokai Ye

Large deep neural network (DNN) models pose the key challenge to energy efficiency due to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or SRAM operations. It motivates the intensive research on model compression wit ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022